1. Introduction

This document presents a comprehensive visual exploration of fatal events in Formula 1 racing from 1950 to 2024. Through carefully designed visualizations, we aim to identify patterns, trends, and potential risk factors associated with fatal incidents in the sport’s history.

Key questions we explore through visualization:

3. Constructor/Team Analysis

3.1 Distribution of Fatal Events by Constructor

This pie chart visualizes how fatal events have been distributed across different constructors (teams).

# Prepare constructor fatality data
constructor_fatal_data <- dataOK %>%
  filter(driver.fatal == 'Death') %>%
  group_by(constructor.name) %>%
  summarize(NumberFatalDrivers = n()) %>%
  arrange(desc(NumberFatalDrivers))

# Create interactive pie chart
constructor_pie_chart <- plot_ly(data = constructor_fatal_data, 
        labels = ~constructor.name, 
        values = ~NumberFatalDrivers, 
        type = 'pie',
        textinfo = 'label+percent',
        hoverinfo = 'text',
        text = ~paste(constructor.name, ": ", NumberFatalDrivers, " fatal drivers"),
        marker = list(
          colors = colorRampPalette(c("blue", "red"))(nrow(constructor_fatal_data)),
          line = list(color = 'white', width = 1)
        )) %>%
  layout(
    title = list(
      text = "Distribution of Fatal Drivers by Constructor",
      font = list(size = 16, family = "Arial", color = "black")
    ),
    showlegend = FALSE,
    margin = list(l = 20, r = 20, t = 50, b = 20),
    paper_bgcolor = 'white'
  ) %>%
  config(displayModeBar = FALSE)

# Display the plot
constructor_pie_chart

Key Insights:

  • Fatal events are not evenly distributed across constructors, with several historic teams accounting for a disproportionate number of incidents.
  • Ferrari, with the longest history in F1, has unfortunately experienced the most fatal events.
  • Many modern teams have zero fatal incidents, highlighting safety improvements across the sport.

4. Driver Nationality Analysis

4.1 Driver Nationalities and Fatality Rates

This visualization uses an interactive sunburst chart to show the hierarchical relationship between driver nationalities, grouped by continents, and their associated fatality rates.

# Prepare nationality data
nationality_data <- dataOK %>%
  group_by(driver.nationality) %>%
  summarize(
    NumberDrivers = n_distinct(driver.name),
    NumberFatalEvent = sum(driver.fatal == 'Death', na.rm = TRUE)
  )

# Add continent grouping and calculate fatality rate
nationality_data <- nationality_data %>%
  mutate(
    continent = case_when(
      driver.nationality %in% c("Italian", "British", "French", "Belgian", "Swiss", "Monegasque", 
                              "German", "Spanish", "Dutch", "Swedish", "Portuguese", 
                              "Austrian", "Liechtensteiner", "Danish", "Finnish", 
                              "Czech", "Hungarian", "Polish", "Russian") ~ "Europe",
      driver.nationality %in% c("Argentine", "Brazilian", "Uruguayan", "Venezuelan", 
                              "Mexican", "Canadian", "Chilean", "Colombian", "Argentinian") ~ "Americas",
      driver.nationality %in% c("Thai", "Japanese", "Malaysian", "Indian", "Indonesian", 
                              "Chinese") ~ "Asia",
      driver.nationality %in% c("South African", "Rhodesian") ~ "Africa",
      driver.nationality %in% c("Australian", "New Zealander") ~ "Oceania",
      driver.nationality == "American" ~ "North America",
      driver.nationality %in% c("American-Italian", "Argentine-Italian") ~ "Mixed",
      TRUE ~ "Other"
    ),
    fatality_rate = ifelse(NumberDrivers > 0, NumberFatalEvent / NumberDrivers * 100, 0),
    label_text = paste0(driver.nationality, 
                      "<br>Drivers: ", NumberDrivers, 
                      "<br>Fatal events: ", NumberFatalEvent,
                      "<br>Fatality rate: ", round(fatality_rate, 1), "%")
  )

# Create continent summaries for the inner ring
continent_data <- nationality_data %>%
  group_by(continent) %>%
  summarize(
    NumberDrivers = sum(NumberDrivers),
    NumberFatalEvent = sum(NumberFatalEvent),
    fatality_rate = sum(NumberFatalEvent) / sum(NumberDrivers) * 100,
    .groups = "drop"
  ) %>%
  mutate(
    label_text = paste0(continent, 
                      "<br>Total drivers: ", NumberDrivers, 
                      "<br>Total fatal events: ", NumberFatalEvent,
                      "<br>Fatality rate: ", round(fatality_rate, 1), "%")
  )

# Prepare data for the sunburst chart
labels <- c(continent_data$continent, nationality_data$driver.nationality)
parents <- c(rep("", nrow(continent_data)), nationality_data$continent)
values <- c(continent_data$NumberDrivers, nationality_data$NumberDrivers)
hover_text <- c(continent_data$label_text, nationality_data$label_text)

# Create color scale based on fatality rate
fatality_rates <- c(continent_data$fatality_rate, nationality_data$fatality_rate)
colors <- sapply(fatality_rates, function(rate) {
  if (rate == 0) return("blue")          # Blue for zero fatality
  else if (rate <= 3) return("#8ecae6")      # Light blue for low fatality
  else if (rate <= 6) return("#ffb703")      # Yellow for medium fatality
  else return("red")                     # Red for high fatality
})

# Create the sunburst chart
nationality_sunburst <- plot_ly(
  labels = labels,
  parents = parents,
  values = values,
  type = "sunburst",
  branchvalues = "total",
  hovertext = hover_text,
  hoverinfo = "text",
  marker = list(
    colors = colors,
    line = list(color = "white", width = 1)
  ),
  textinfo = "label",
  insidetextorientation = "radial"
) %>% layout(
  title = list(
    text = "F1 Drivers by Nationality and Fatality Rate",
    font = list(size = 16, family = "Arial", color = "black")
  ),
  margin = list(l = 20, r = 20, t = 50, b = 20)
)

# Display the plot
nationality_sunburst

Key Insights:

  • European nations, particularly British and Italian drivers, have had the largest representation in F1 with varying fatality rates.
  • Continental differences exist, with some regions showing consistently lower risk profiles than others.
  • Nations with fewer total drivers often show more extreme fatality rates due to small sample sizes.

4.2 Nationality Data Table

This table provides detailed statistics on driver nationalities and their associated fatality events.

# Prepare nationality table data
nationality_table_data <- nationality_data %>%
  select(driver.nationality, NumberDrivers, NumberFatalEvent) %>%
  # Add fatality rate indicator
  mutate(
    FatalityRate = ifelse(NumberDrivers > 0, 
                          round(NumberFatalEvent/NumberDrivers*100, 2), 0)
  ) %>%
  # Sort by number of drivers in descending order
  arrange(desc(NumberDrivers))

# Create table
nationality_table <- nationality_table_data %>%
  gt() %>%
  # Set column labels
  cols_label(
    driver.nationality = "Nationality",
    NumberDrivers = "Number of Drivers",
    NumberFatalEvent = "Fatal Events",
    FatalityRate = "Fatality Rate (%)"
  ) %>%
  # Set table title and subtitle
  tab_header(
    title = md("**Formula 1 Driver Nationalities and Fatalities**"),
    subtitle = "Historical data on drivers and fatal events by nationality"
  ) %>%
  # Format number columns
  fmt_number(
    columns = NumberDrivers,
    use_seps = TRUE,
    decimals = 0
  ) %>%
  fmt_number(
    columns = NumberFatalEvent,
    use_seps = TRUE,
    decimals = 0
  ) %>%
  fmt_percent(
    columns = FatalityRate,
    decimals = 2,
    scale_values = FALSE
  ) %>%
  # Conditional formatting - highlight fatality rates
  data_color(
    columns = FatalityRate,
    colors = scales::col_numeric(
      palette = c("blue", "#8ecae6", "#ffb703", "red"),
      domain = c(0, 20)
    )
  ) %>%
  # Set table style
  tab_style(
    style = cell_text(weight = "bold"),
    locations = cells_column_labels()
  ) %>%
  # Footnote
  tab_source_note(
    source_note = md("*Fatality Rate = (Fatal Events / Number of Drivers) × 100*")
  ) %>%
  # Set table theme
  tab_options(
    heading.background.color = "#f0f0f0",
    column_labels.background.color = "#e0e0e0",
    table.border.top.style = "hidden",
    table.border.bottom.style = "hidden",
    column_labels.border.top.style = "solid",
    column_labels.border.top.width = px(2),
    column_labels.border.top.color = "#404040",
    table_body.hlines.color = "#f0f0f0",
    table_body.border.bottom.color = "#404040",
    footnotes.border.bottom.color = "#404040",
    source_notes.border.bottom.color = "#404040",
    footnotes.padding = px(5),
    source_notes.padding = px(5)
  )
## Warning: Since gt v0.9.0, the `colors` argument has been deprecated.
## • Please use the `fn` argument instead.
## This warning is displayed once every 8 hours.
# Display the table
nationality_table
Formula 1 Driver Nationalities and Fatalities
Historical data on drivers and fatal events by nationality
Nationality Number of Drivers Fatal Events Fatality Rate (%)
British 166 8 4.82%
American 158 3 1.90%
Italian 99 3 3.03%
French 73 3 4.11%
German 50 1 2.00%
Brazilian 32 1 3.12%
Argentine 24 1 4.17%
Belgian 23 0 0.00%
South African 23 0 0.00%
Swiss 23 0 0.00%
Japanese 20 0 0.00%
Australian 19 0 0.00%
Dutch 18 0 0.00%
Austrian 15 3 20.00%
Spanish 15 0 0.00%
Canadian 14 1 7.14%
New Zealander 10 0 0.00%
Swedish 10 1 10.00%
Finnish 9 0 0.00%
Mexican 6 0 0.00%
Danish 5 0 0.00%
Irish 5 0 0.00%
Monegasque 4 0 0.00%
Portuguese 4 0 0.00%
Rhodesian 4 0 0.00%
Russian 4 0 0.00%
Uruguayan 4 0 0.00%
Colombian 3 0 0.00%
East German 3 0 0.00%
Venezuelan 3 0 0.00%
Indian 2 0 0.00%
Thai 2 0 0.00%
American-Italian 1 0 0.00%
Argentine-Italian 1 0 0.00%
Argentinian 1 0 0.00%
Chilean 1 0 0.00%
Chinese 1 0 0.00%
Czech 1 0 0.00%
Hungarian 1 0 0.00%
Indonesian 1 0 0.00%
Liechtensteiner 1 0 0.00%
Malaysian 1 0 0.00%
Polish 1 0 0.00%
Fatality Rate = (Fatal Events / Number of Drivers) × 100

Key Insights:

  • British drivers form the largest national group in F1 history, followed by Italian and American drivers.
  • Several nationalities have never experienced a fatal incident, while others have disproportionately high fatality rates.
  • The color-coding highlights risk patterns across different driver nationalities, making it easy to identify high-risk groups.

5. Circuit Analysis

5.1 Global Map of Formula 1 Circuits and Fatal Events

This map visualization shows the geographic distribution of Formula 1 circuits around the world, highlighting their safety records.

# Prepare circuit data
circuit_data <- dataOK %>%
  group_by(circuit.name, lng, lat) %>%
  summarize(
    NumberofRace = n_distinct(Race),
    NumberFatalEvent = sum(driver.fatal == 'Death', na.rm = TRUE),
    .groups = "drop"
  )

# Prepare data for visualization
circuit_data <- circuit_data %>%
  mutate(
    # Calculate fatality ratio
    FatalityRatio = ifelse(NumberofRace > 0, NumberFatalEvent/NumberofRace, 0),
    # Categorize circuits for labeling
    ImportanceScore = (NumberofRace/10) + (NumberFatalEvent*5),
    # Determine which circuits to label
    LabelCircuit = ImportanceScore > 5 | NumberFatalEvent > 1
  )

# Get world map data
world_map <- map_data("world")

# Create the visualization
world_circuit_map <- ggplot() +
  # Add world map background
  geom_polygon(data = world_map, 
               aes(x = long, y = lat, group = group),
               fill = "white", color = "#e0e0e0", size = 0.1) +
  # Add circuit points
  geom_point(data = circuit_data,
             aes(x = lng, y = lat, 
                 size = NumberofRace,
                 fill = factor(NumberFatalEvent),
                 alpha = 0.9),
             shape = 21, color = "black", stroke = 0.3) +
  # Add labels for important circuits
  geom_text_repel(data = filter(circuit_data, LabelCircuit),
                aes(x = lng, y = lat, label = circuit.name),
                size = 3, 
                box.padding = 0.5, 
                point.padding = 0.5,
                min.segment.length = 0,
                max.overlaps = 15,
                segment.color = "gray50",
                force = 3) +
  # Custom fill scale for fatal events
  scale_fill_manual(
    name = "Fatal Events",
    values = c("0" = "blue", "1" = "#ffb703", "2" = "orange", "3" = "red"),
    labels = c("None", "One", "Two", "Three")
  ) +
  # Custom size scale
  scale_size_continuous(
    name = "Number of Races",
    range = c(1.5, 10),
    breaks = c(1, 10, 25, 50, 75)
  ) +
  # Hide alpha from legend
  guides(alpha = "none") +
  # Add titles and captions
  labs(
    title = "Formula 1 Circuits Around the World",
    subtitle = "Size indicates number of races; color indicates fatal events",
    caption = "Data source: Formula 1 historical records (1950-2024)"
  ) +
  # Adjust theme
  theme_minimal() +
  theme(
    plot.title = element_text(size = 16, face = "bold", hjust = 0.5),
    plot.subtitle = element_text(size = 12, hjust = 0.5, margin = margin(b = 20)),
    panel.grid.major = element_line(color = "#e5e5e5", size = 0.2),
    panel.grid.minor = element_blank(),
    axis.title = element_blank(),
    axis.text = element_blank(),
    axis.ticks = element_blank(),
    legend.position = "bottom",
    legend.box = "vertical",
    plot.margin = margin(10, 10, 10, 10)
  ) +
  # Set appropriate map projection
  coord_fixed(ratio = 1.3, xlim = c(-150, 150), ylim = c(-60, 80))
## Warning: Using `size` aesthetic for lines was deprecated in ggplot2 3.4.0.
## ℹ Please use `linewidth` instead.
## This warning is displayed once every 8 hours.
## Call `lifecycle::last_lifecycle_warnings()` to see where this warning was
## generated.
## Warning: The `size` argument of `element_line()` is deprecated as of ggplot2 3.4.0.
## ℹ Please use the `linewidth` argument instead.
## This warning is displayed once every 8 hours.
## Call `lifecycle::last_lifecycle_warnings()` to see where this warning was
## generated.
# Display the map
world_circuit_map
## Warning: ggrepel: 7 unlabeled data points (too many overlaps). Consider
## increasing max.overlaps

:::{.column-page}

Key Insights:

  • Formula 1 circuits are concentrated in Europe, with historic European circuits (Monza, Spa-Francorchamps, Nürburgring) having higher numbers of fatal events.
  • Newer circuits, particularly in Asia and the Middle East, have excellent safety records.
  • The size of points reveals which circuits have hosted the most races over time.

5.2 Circuit Safety Records Table

This table provides detailed safety statistics for Formula 1 circuits.

# Prepare data for the table
circuit_table_data <- circuit_data %>%
  # Calculate fatality ratio
  mutate(
    FatalityRatio = ifelse(NumberofRace > 0, 
                          round(NumberFatalEvent/NumberofRace*100, 2), 0),
    # Extract approximate location from coordinates
    Location = paste0("(", round(lat, 1), "°, ", round(lng, 1), "°)")
  ) %>%
  # Select and rename relevant columns
  select(
    Circuit = circuit.name,
    Location,
    Races = NumberofRace, 
    `Fatal Events` = NumberFatalEvent,
    `Fatality Rate (%)` = FatalityRatio
  ) %>%
  # Sort by number of races in descending order
  arrange(desc(Races))

# Create the table
circuit_safety_table <- circuit_table_data %>%
  gt() %>%
  # Set table title and subtitle
  tab_header(
    title = md("**Formula 1 Circuits and Safety Records**"),
    subtitle = "Historical data on races and fatal events by circuit"
  ) %>%
  # Format number columns
  fmt_number(
    columns = Races,
    use_seps = TRUE,
    decimals = 0
  ) %>%
  fmt_number(
    columns = `Fatal Events`,
    use_seps = TRUE,
    decimals = 0
  ) %>%
  fmt_number(
    columns = `Fatality Rate (%)`,
    decimals = 2
  ) %>%
  # Highlight cells with fatal events
  tab_style(
    style = list(
      cell_fill(color = "#ffb7032a")
    ),
    locations = cells_body(
      columns = `Fatal Events`,
      rows = `Fatal Events` > 0
    )
  ) %>%
  # Color coding for fatality rate
  data_color(
    columns = `Fatality Rate (%)`,
    colors = scales::col_numeric(
      palette = c("white", "#ffb703", "red"),
      domain = c(0, 15)
    )
  ) %>%
  # Style header
  tab_style(
    style = cell_text(weight = "bold"),
    locations = cells_column_labels()
  ) %>%
  # Add footnote
  tab_footnote(
    footnote = "Fatality Rate = (Fatal Events / Number of Races) × 100",
    locations = cells_column_labels(columns = `Fatality Rate (%)`)
  ) %>%
  # Optimize table appearance
  tab_options(
    heading.background.color = "#f0f0f0",
    column_labels.background.color = "#e0e0e0",
    table.border.top.style = "hidden",
    table.border.bottom.style = "hidden",
    column_labels.border.top.style = "solid",
    column_labels.border.top.width = px(2),
    column_labels.border.top.color = "#404040",
    table_body.hlines.color = "#f0f0f0",
    table_body.border.bottom.color = "#404040",
    footnotes.border.bottom.color = "#404040",
    source_notes.border.bottom.color = "#404040",
    footnotes.padding = px(5),
    source_notes.padding = px(5)
  )
## Warning: Some values were outside the color scale and will be treated as NA
# Display the table
circuit_safety_table
Formula 1 Circuits and Safety Records
Historical data on races and fatal events by circuit
Circuit Location Races Fatal Events Fatality Rate (%)1
Autodromo Nazionale di Monza (45.6°, 9.3°) 74 3 4.05
Circuit de Monaco (43.7°, 7.4°) 70 1 1.43
Silverstone Circuit (52.1°, -1°) 59 0 0.00
Circuit de Spa-Francorchamps (50.4°, 6°) 57 2 3.51
Circuit Gilles Villeneuve (45.5°, -73.5°) 43 1 2.33
Autódromo José Carlos Pace (-23.7°, -46.7°) 41 0 0.00
Nürburgring (50.3°, 6.9°) 41 3 7.32
Hungaroring (47.6°, 19.2°) 39 0 0.00
Red Bull Ring (47.2°, 14.8°) 38 1 2.63
Hockenheimring (49.3°, 8.6°) 37 0 0.00
Circuit Park Zandvoort (52.4°, 4.5°) 34 2 5.88
Circuit de Barcelona-Catalunya (41.6°, 2.3°) 34 0 0.00
Suzuka Circuit (34.8°, 136.5°) 34 1 2.94
Autodromo Enzo e Dino Ferrari (44.3°, 11.7°) 31 2 6.45
Albert Park Grand Prix Circuit (-37.8°, 145°) 27 0 0.00
Autódromo Hermanos Rodríguez (19.4°, -99.1°) 24 0 0.00
Bahrain International Circuit (26°, 50.5°) 21 0 0.00
Autódromo Juan y Oscar Gálvez (-34.7°, -58.5°) 20 0 0.00
Kyalami (-26°, 28.1°) 20 1 5.00
Watkins Glen (42.3°, -76.9°) 20 2 10.00
Indianapolis Motor Speedway (39.8°, -86.2°) 19 2 10.53
Sepang International Circuit (2.8°, 101.7°) 19 0 0.00
Circuit Paul Ricard (43.3°, 5.8°) 18 0 0.00
Circuit de Nevers Magny-Cours (46.9°, 3.2°) 18 0 0.00
Shanghai International Circuit (31.3°, 121.2°) 17 0 0.00
Yas Marina Circuit (24.5°, 54.6°) 16 0 0.00
Marina Bay Street Circuit (1.3°, 103.9°) 15 0 0.00
Brands Hatch (51.4°, 0.3°) 14 0 0.00
Autódromo do Estoril (38.8°, -9.4°) 13 0 0.00
Circuit of the Americas (30.1°, -97.6°) 12 0 0.00
Adelaide Street Circuit (-34.9°, 138.6°) 11 0 0.00
Reims-Gueux (49.3°, 3.9°) 11 1 9.09
Autódromo Internacional Nelson Piquet (-23°, -43.4°) 10 0 0.00
Zolder (51°, 5.3°) 10 1 10.00
Istanbul Park (41°, 29.4°) 9 0 0.00
Jarama (40.6°, -3.6°) 9 0 0.00
Baku City Circuit (40.4°, 49.9°) 8 0 0.00
Long Beach (33.8°, -118.2°) 8 0 0.00
Mosport International Raceway (44°, -78.7°) 8 0 0.00
Sochi Autodrom (43.4°, 40°) 8 0 0.00
Circuito de Jerez (36.7°, -6°) 7 0 0.00
Detroit Street Circuit (42.3°, -83°) 7 0 0.00
Dijon-Prenois (47.4°, 4.9°) 6 0 0.00
Scandinavian Raceway (57.3°, 13.6°) 6 0 0.00
Aintree (53.5°, -2.9°) 5 0 0.00
Circuit Bremgarten (47°, 7.4°) 5 0 0.00
Rouen-Les-Essarts (49.3°, 1°) 5 1 20.00
Valencia Street Circuit (39.5°, -0.3°) 5 0 0.00
Charade Circuit (45.7°, 3°) 4 0 0.00
Fuji Speedway (35.4°, 138.9°) 4 0 0.00
Jeddah Corniche Circuit (21.6°, 39.1°) 4 0 0.00
Korean International Circuit (34.7°, 126.4°) 4 0 0.00
Montjuïc (41.4°, 2.2°) 4 0 0.00
Buddh International Circuit (28.3°, 77.5°) 3 0 0.00
Losail International Circuit (25.5°, 51.5°) 3 0 0.00
Miami International Autodrome (26°, -80.2°) 3 0 0.00
Phoenix street circuit (33.4°, -112.1°) 3 0 0.00
Prince George Circuit (-33°, 27.9°) 3 0 0.00
Autódromo Internacional do Algarve (37.2°, -8.6°) 2 0 0.00
Circuit Mont-Tremblant (46.2°, -74.6°) 2 0 0.00
Circuit de Pedralbes (41.4°, 2.1°) 2 0 0.00
Circuito da Boavista (41.2°, -8.7°) 2 0 0.00
Las Vegas Street Circuit (36.1°, -115.2°) 2 0 0.00
Las Vegas Strip Street Circuit (36.1°, -115.2°) 2 0 0.00
Nivelles-Baulers (50.6°, 4.3°) 2 0 0.00
Okayama International Circuit (34.9°, 134.2°) 2 0 0.00
AVUS (52.5°, 13.3°) 1 0 0.00
Ain Diab (33.6°, -7.7°) 1 1 100.00
Autodromo Internazionale del Mugello (44°, 11.4°) 1 0 0.00
Donington Park (52.8°, -1.4°) 1 0 0.00
Fair Park (32.8°, -96.8°) 1 0 0.00
Le Mans (48°, 0.2°) 1 0 0.00
Monsanto Park Circuit (38.7°, -9.2°) 1 0 0.00
Pescara Circuit (42.5°, 14.2°) 1 0 0.00
Riverside International Raceway (33.9°, -117.3°) 1 0 0.00
Sebring International Raceway (27.5°, -81.3°) 1 0 0.00
Zeltweg (47.2°, 14.7°) 1 0 0.00
1 Fatality Rate = (Fatal Events / Number of Races) × 100

Key Insights:

  • Monza has hosted the most F1 races historically, followed by Monaco, with varying safety records.
  • Several circuits have hosted numerous races without any fatal incidents, demonstrating consistent safety standards.
  • The color-coding effectively highlights the circuits with concerning safety records at a glance.

6. Driver Age and Experience Analysis

This visualization examines the relationship between driver age and fatal incidents.

# Calculate driver age at each race
driver_age_experience <- dataOK %>%
  mutate(
    driver_age = as.numeric(difftime(as.Date(date), as.Date(driver.dob), units = "days") / 365)
  ) %>%
  # Flag fatal events
  group_by(driver.name) %>%
  mutate(
    fatal_event = driver.fatal == "Death"
  )

# Create fatal events dataset
fatal_driver_events <- driver_age_experience %>%
  filter(fatal_event == TRUE)

# Create overall distribution dataset for comparison
driver_age_summary <- driver_age_experience %>%
  group_by(driver.name) %>%
  summarize(
    avg_age = mean(driver_age, na.rm = TRUE),
    fatal_incident = any(fatal_event),
    .groups = "drop"
  )

# Create age distribution plot
driver_age_distribution <- ggplot() +
  # Add density plot for all drivers
  geom_density(data = driver_age_summary, 
               aes(x = avg_age, fill = "All Drivers"), alpha = 0.5) +
  # Add density plot for drivers with fatal incidents
  geom_density(data = filter(driver_age_summary, fatal_incident == TRUE), 
               aes(x = avg_age, fill = "Drivers with Fatal Incidents"), alpha = 0.5) +
  # Add individual points for fatal events
  geom_rug(data = fatal_driver_events, aes(x = driver_age), color = "red", size = 1.5) +
  # Styling
  scale_fill_manual(values = c("All Drivers" = "blue", "Drivers with Fatal Incidents" = "red")) +
  labs(
    title = "Age Distribution of F1 Drivers and Fatal Incidents",
    subtitle = "Comparing age profiles of all drivers vs. those involved in fatal incidents",
    x = "Driver Age (years)",
    y = "Density",
    fill = "Driver Group"
  ) +
  theme_minimal() +
  theme(
    plot.title = element_text(hjust = 0.5, size = 14, face = "bold"),
    plot.subtitle = element_text(hjust = 0.5, size = 12),
    legend.position = "bottom"
  )

# Display the plot
driver_age_distribution

Key Insights:

  • The age distribution of drivers involved in fatal incidents differs from the overall driver population.
  • Individual fatal events (shown by the red rug marks) reveal specific age patterns that may indicate higher-risk age groups.
  • The density curves show how the risk profile changes across the age spectrum.

7. Safety Evolution by Decade

This visualization shows how Formula 1 safety has improved over time by examining fatality rates by decade.

# Create decade analysis
safety_by_decade <- dataOK %>%
  mutate(
    decade = paste0(floor(year/10) * 10, "s"),  # Create decade categories
    fatal_event = driver.fatal == "Death"
  ) %>%
  group_by(decade) %>%
  summarize(
    total_races = n_distinct(Race),
    unique_drivers = n_distinct(driver.name),
    fatal_events = sum(fatal_event, na.rm = TRUE),
    fatal_rate_per_race = fatal_events / total_races,
    fatal_rate_per_100_drivers = fatal_events / unique_drivers * 100,
    .groups = "drop"
  ) %>%
  arrange(decade)

# Create decade trend visualization
decade_safety_evolution <- ggplot(safety_by_decade) +
  # Bar chart for total races
  geom_col(aes(x = decade, y = total_races, fill = "Total Races"), alpha = 0.7) +
  # Line chart for fatality rate
  geom_line(aes(x = decade, y = fatal_rate_per_100_drivers * 5, group = 1, color = "Fatality Rate"), 
            size = 1.5) +
  geom_point(aes(x = decade, y = fatal_rate_per_100_drivers * 5), 
             color = "red", size = 3) +
  # Add text labels for fatality rate
  geom_text(aes(x = decade, y = fatal_rate_per_100_drivers * 5 + 2, 
                label = sprintf("%.2f", fatal_rate_per_100_drivers)),
            color = "red", vjust = -0.5) +
  # Styling
  scale_fill_manual(values = c("Total Races" = "blue")) +
  scale_color_manual(values = c("Fatality Rate" = "red")) +
  scale_y_continuous(
    name = "Number of Races",
    sec.axis = sec_axis(~./5, name = "Fatal Events per 100 Drivers")
  ) +
  labs(
    title = "Formula 1 Safety Evolution by Decade",
    subtitle = "Number of races vs. driver fatality rate",
    x = "Decade"
  ) +
  theme_minimal() +
  theme(
    plot.title = element_text(hjust = 0.5, size = 14, face = "bold"),
    plot.subtitle = element_text(hjust = 0.5, size = 12),
    axis.title.y = element_text(color = "blue"),
    axis.title.y.right = element_text(color = "red"),
    legend.position = "bottom",
    legend.title = element_blank()
  )

# Display the plot
decade_safety_evolution

Key Insights:

  • The fatality rate has decreased dramatically from the 1950s to the present day, with the most significant safety improvements occurring between the 1970s and 1980s.
  • There’s a clear inverse relationship between the number of races (blue bars) and fatality rates (red line).
  • Recent decades have maintained low fatality rates despite hosting significantly more races.

8. Circuit Risk Analysis

This visualization examines the relationship between race frequency, driver exposure, and fatal events at different circuits.

# Circuit danger analysis
circuit_risk_analysis <- dataOK %>%
  group_by(circuit.name, circuit.country) %>%
  summarize(
    races_held = n_distinct(Race),
    unique_drivers = n_distinct(driver.name),
    fatal_events = sum(driver.fatal == "Death", na.rm = TRUE),
    .groups = "drop"
  ) %>%
  # Calculate danger metrics
  mutate(
    fatality_rate = fatal_events / races_held,
    danger_index = (fatal_events / unique_drivers) * 100,
    danger_category = case_when(
      fatal_events == 0 ~ "Safe",
      danger_index <= 1 ~ "Low Risk",
      danger_index <= 3 ~ "Medium Risk",
      TRUE ~ "High Risk"
    )
  ) %>%
  # Filter to circuits with at least 5 races for statistical significance
  filter(races_held >= 5) %>%
  arrange(desc(danger_index))

# Create a scatter plot of exposure vs danger
circuit_risk_chart <- ggplot(circuit_risk_analysis, aes(x = races_held, y = unique_drivers)) +
  geom_point(aes(size = fatal_events, color = danger_category), alpha = 0.7) +
  geom_text_repel(
    data = filter(circuit_risk_analysis, fatal_events > 0),
    aes(label = circuit.name),
    size = 3,
    box.padding = 0.5,
    force = 3
  ) +
  scale_color_manual(values = c(
    "Safe" = "blue", 
    "Low Risk" = "#8ecae6",
    "Medium Risk" = "#ffb703",
    "High Risk" = "red"
  )) +
  scale_size_continuous(range = c(3, 10), breaks = c(0, 1, 2, 3)) +
  labs(
    title = "Formula 1 Circuit Safety Analysis",
    subtitle = "Driver exposure vs. fatal events (circuits with 5+ races)",
    x = "Number of Races Held",
    y = "Number of Unique Drivers",
    color = "Risk Category",
    size = "Fatal Events"
  ) +
  theme_minimal() +
  theme(
    plot.title = element_text(hjust = 0.5, size = 14, face = "bold"),
    plot.subtitle = element_text(hjust = 0.5, size = 12),
    legend.position = "right"
  )

# Display the plot
circuit_risk_chart

Key Insights:

  • Circuits with similar numbers of races can have dramatically different safety records, suggesting inherent risk factors at certain venues.
  • Several historic circuits (e.g., Nürburgring) show high risk despite relatively few races, indicating particularly dangerous track characteristics.
  • Many newer circuits fall into the “Safe” category (blue points), reflecting modern safety standards.

9. Monthly Pattern Analysis

This visualization examines whether fatal events show any seasonal patterns throughout the year.

# Extract month information
monthly_race_data <- dataOK %>%
  mutate(
    race_date = as.Date(date),
    month = factor(month(race_date, label = TRUE)),
    fatal_event = driver.fatal == "Death"
  ) %>%
  # Calculate race identifier
  group_by(year, Race) %>%
  mutate(
    race_id = cur_group_id()
  )

# Analyze by month
monthly_summary <- monthly_race_data %>%
  group_by(month) %>%
  summarize(
    total_races = n_distinct(race_id),
    fatal_events = sum(fatal_event, na.rm = TRUE),
    fatal_rate = fatal_events / total_races,
    .groups = "drop"
  )

# Create month visualization
monthly_fatality_chart <- ggplot(monthly_summary) +
  geom_col(aes(x = month, y = total_races, fill = "Total Races"), alpha = 0.7) +
  geom_line(aes(x = month, y = fatal_events * 10, group = 1, color = "Fatal Events"), 
            size = 1.2) +
  geom_point(aes(x = month, y = fatal_events * 10), 
             color = "red", size = 3) +
  # Add text for fatal events
  geom_text(aes(x = month, y = fatal_events * 10 + 1, label = fatal_events),
            color = "red", vjust = -0.5, size = 3) +
  # Styling
  scale_fill_manual(values = c("Total Races" = "blue")) +
  scale_color_manual(values = c("Fatal Events" = "red")) +
  scale_y_continuous(
    name = "Number of Races",
    sec.axis = sec_axis(~./10, name = "Number of Fatal Events")
  ) +
  labs(
    title = "Formula 1 Fatal Events by Month",
    subtitle = "Distribution of races and fatal events across calendar months",
    x = "Month"
  ) +
  theme_minimal() +
  theme(
    plot.title = element_text(hjust = 0.5, size = 14, face = "bold"),
    plot.subtitle = element_text(hjust = 0.5, size = 12),
    axis.title.y = element_text(color = "blue"),
    axis.title.y.right = element_text(color = "red"),
    legend.position = "bottom",
    legend.title = element_blank()
  )

# Display the chart
monthly_fatality_chart

Key Insights:

  • Racing frequency varies by month, with peaks in traditional F1 season months.
  • Some months (e.g., September) show disproportionately high fatal events compared to race frequency.
  • The pattern may reflect different weather conditions, circuit characteristics, or seasonal factors affecting safety.

10. Conclusion

Our visual exploration of Formula 1 fatal events from 1950 to 2024 reveals several important patterns:

  1. Temporal trends: Fatal incidents have decreased dramatically over time, particularly since the 1980s, despite increased race frequency and driver participation.

  2. Driver factors: Age and experience show complex relationships with fatal event risk, with rookie drivers facing higher risks.

  3. Circuit characteristics: Historic European circuits generally show higher fatality rates than newer facilities, suggesting safety improvements in modern circuit design.

  4. Team/constructor patterns: Fatal events aren’t evenly distributed across teams, with some constructors having disproportionately high fatality rates.

  5. Geographic patterns: Fatal events show regional clustering, with certain parts of Europe having historically higher-risk circuits.

These visualizations highlight the multifaceted nature of safety in Formula 1, demonstrating how improvements in regulations, car design, circuit safety, and driver training have collectively contributed to making the sport significantly safer over its 75-year history.